Retinal vessel segmentation using multiwavelet kernels and multiscale hierarchical decomposition

Authors:
Yangfan Wang;Guangrong Ji;Ping Lin;Emanuele Trucco
Affiliations:
College of Marine Life Science, Ocean University of China, Qingdao 266100, PR China and Division of Mathematics, University of Dundee, Dundee DD1 4HN, UK and Department of Mathematics, University ...;College of Information, Ocean University of China, Qingdao 266100, PR China;Division of Mathematics, University of Dundee, Dundee DD1 4HN, UK and Department of Mathematics, University of Science and Technology Beijing, Beijing 100083, PR China;School of Computing, University of Dundee, Dundee DD1 4HN, UK
Venue:
Pattern Recognition
Year:
2013

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Abstract

We propose a comprehensive method for segmenting the retinal vasculature in fundus camera images. Our method does not require preprocessing and training and can therefore be used directly on different images sets. We enhance the vessels using matched filtering with multiwavelet kernels (MFMK), separating vessels from clutter and bright, localized features. Noise removal and vessel localization are achieved by a multiscale hierarchical decomposition of the normalized enhanced image. We show a necessary condition to achieve the optimal decomposition and derive the associated value of the scale parameter controlling the amount of details captured. Finally, we obtain a binary map of the vasculature by locally adaptive thresholding, generating a threshold surface based on the vessel edge information extracted by the previous processes. We report experimental results on two public retinal data sets, DRIVE and STARE, demonstrating an excellent performance in comparison with retinal vessel segmentation methods reported recently.